Artificial neural network for the prediction model of glomerular filtration rate to estimate the normal or abnormal stages of kidney using gamma camera
© 2021. The Japanese Society of Nuclear Medicine..
OBJECTIVE: Chronic kidney disease (CKD) is evaluated based on glomerular filtration rate (GFR) using a gamma camera in the nuclear medicine center or hospital in a routine procedure, but the gamma camera does not provide the accurate stages of the diseases. Therefore, this research aimed to find out the normal or abnormal stages of CKD based on the value of GFR using an artificial neural network (ANN).
METHODS: Two hundred fifty (Training 188, Testing 62) kidney patients who underwent the ultrasonography test to diagnose the renal test in our nuclear medical centre were scanned using gamma camera. The patients were injected with 99mTc-DTPA before the scanning procedure. After pushing the syringe into the patient's vein, the pre-syringe and post syringe radioactive counts were calculated using the gamma camera. The artificial neural network uses the softmax function with cross-entropy loss to diagnose CKD normal or abnormal labels depending on the value of GFR in the output layer.
RESULTS: The results showed that the accuracy of the proposed ANN model was 99.20% for K-fold cross-validation. The sensitivity and specificity were 99.10% and 99.20%, respectively. The Area under the curve (AUC) was 0.9994.
CONCLUSION: The proposed model using an artificial neural network can classify the normal or abnormal stages of CKD. After implementing the proposed model clinically, it may upgrade the gamma camera to diagnose the normal or abnormal stages of the CKD with an appropriate GFR value.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:35 |
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| Enthalten in: |
Annals of nuclear medicine - 35(2021), 12 vom: 07. Dez., Seite 1342-1352 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Hossain, Alamgir [VerfasserIn] |
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| Links: |
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| Themen: |
Artificial neural network |
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| Anmerkungen: |
Date Completed 03.02.2022 Date Revised 03.02.2022 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1007/s12149-021-01676-7 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM330339060 |
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| 100 | 1 | |a Hossain, Alamgir |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Artificial neural network for the prediction model of glomerular filtration rate to estimate the normal or abnormal stages of kidney using gamma camera |
| 264 | 1 | |c 2021 | |
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| 500 | |a Date Completed 03.02.2022 | ||
| 500 | |a Date Revised 03.02.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2021. The Japanese Society of Nuclear Medicine. | ||
| 520 | |a OBJECTIVE: Chronic kidney disease (CKD) is evaluated based on glomerular filtration rate (GFR) using a gamma camera in the nuclear medicine center or hospital in a routine procedure, but the gamma camera does not provide the accurate stages of the diseases. Therefore, this research aimed to find out the normal or abnormal stages of CKD based on the value of GFR using an artificial neural network (ANN) | ||
| 520 | |a METHODS: Two hundred fifty (Training 188, Testing 62) kidney patients who underwent the ultrasonography test to diagnose the renal test in our nuclear medical centre were scanned using gamma camera. The patients were injected with 99mTc-DTPA before the scanning procedure. After pushing the syringe into the patient's vein, the pre-syringe and post syringe radioactive counts were calculated using the gamma camera. The artificial neural network uses the softmax function with cross-entropy loss to diagnose CKD normal or abnormal labels depending on the value of GFR in the output layer | ||
| 520 | |a RESULTS: The results showed that the accuracy of the proposed ANN model was 99.20% for K-fold cross-validation. The sensitivity and specificity were 99.10% and 99.20%, respectively. The Area under the curve (AUC) was 0.9994 | ||
| 520 | |a CONCLUSION: The proposed model using an artificial neural network can classify the normal or abnormal stages of CKD. After implementing the proposed model clinically, it may upgrade the gamma camera to diagnose the normal or abnormal stages of the CKD with an appropriate GFR value | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Artificial neural network | |
| 650 | 4 | |a Gamma camera | |
| 650 | 4 | |a Glomerular filtration rate | |
| 650 | 4 | |a Stages of chronic kidney disease | |
| 700 | 1 | |a Chowdhury, Shariful Islam |e verfasserin |4 aut | |
| 700 | 1 | |a Sarker, Shupti |e verfasserin |4 aut | |
| 700 | 1 | |a Ahsan, Mostofa Shamim |e verfasserin |4 aut | |
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